/*
 *  Copyright (c) 2010 Mathew Hall.
 *  All rights reserved.
 * 
 *  Redistribution and use in source and binary forms, with or
 *  without modification, are permitted provided that the following conditions
 *  are met:
 * 
 *  Redistributions of source code must retain the above copyright
 *  notice, this list of conditions and the following disclaimer.
 *  Redistributions in binary form must reproduce the above
 *  copyright notice, this list of conditions and the following
 *  disclaimer in the documentation and/or other materials provided
 *  with the distribution.
 *  Neither the name of the <ORGANIZATION> nor the names of its
 *  contributors may be used to endorse or promote products derived
 *  from this software without specific prior written permission.
 * 
 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
 *  CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
 *  INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
 *  MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 *  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
 *  CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 *  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
 *  NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
 *  HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 *  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
 *  OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
 *  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */

package search.fitnessfunctions;

import bunchbridge.BunchFitnessAdaptor;
import primitives.cluster.ClusterHead;
import primitives.cluster.*;
import primitives.graph.Node;
import static Math.log;
/**
 *
 * @author Mathew Hall
 */
public class LimitedMQFitnessFunction extends BunchTurboMQFitnessFunction {
    double numChildren(IClusterLevel c){
	if(!c.encapsulates()){
		return c.nodes.size();
	}else{
		return c.children.size();
	}

    }
	public double evaluate(ClusterHead tree){
		double val = super.evaluate(tree)
		def penalties = 0
		double total = 0;

	        def modules = [];


	        tree.getChildren().each{modules.add(it)}

	        IClusterLevel current;
	        def curr = 0;
	        while (curr < modules.size()){
	            current = modules.get(curr)
	            curr++
				if(!current.knownAs(-1) || (!current.encapsulates() && numChildren(current) == 1)){
					if(current.encapsulates())
			            		modules.addAll(current.children)
			            
				    def count = numChildren(current);
				    if(count < 5 || count > 9){
					penalties++
					}
					total++
			        }
	        }
	
		def penalty_amt =1 - (penalties * 1.0/total)
		

		return (double)val * penalty_amt
	}


	public static Boolean subsumes() {
		return true;
	}
	


}
